TWI782943B - A virtual metrology method for esc temperature estimation using thermal control elements - Google Patents

Abstract

A temperature controller for a substrate support in a substrate processing system includes memory that stores a first model correlating temperatures of a plurality of first thermal control elements (TCEs) arranged in the substrate support and first temperature responses of the substrate support. The first temperature responses correspond to locations on a surface of the substrate support. A temperature estimation module calculates resistances of the first TCEs, determines, based on the calculated resistances, the temperatures of the first TCEs, and estimates, using the stored first model and the determined temperatures of the first TCEs, an actual temperature response of the substrate support. The temperature controller is configured to control the first TCEs based on the actual temperature response of the substrate support.

Description

Virtual metrology method for electrostatic chuck temperature estimation using thermal control elements

The present disclosure relates to substrate processing systems, and more particularly to systems and methods for estimating the temperature of a substrate support in a substrate processing system.

The background description provided herein is for the purpose of presenting the context of the disclosure in general terms. The work products of the presently listed inventors (to the extent described in this prior art section), and implementations of the described content that would not otherwise qualify as prior art at the time of filing, are not expressly or It is implicitly admitted that it is prior art with respect to the present disclosure.

Substrate processing systems may be used to perform etching, deposition, and/or other processing of substrates such as semiconductor wafers. Exemplary processes that may be performed on the substrate include, but are not limited to: plasma enhanced chemical vapor deposition (PECVD) process, chemically enhanced plasma vapor deposition (CEPVD) process, sputtering physical vapor deposition (PVD) process, ion implantation process, and/or other etching (eg, chemical etching, plasma etching, reactive ion etching, etc.), deposition, and cleaning processes. In a processing chamber of a substrate processing system, a substrate may be disposed on a substrate support such as a pedestal, electrostatic chuck (ESC), or the like. For example, during etching, a gas mixture including one or more precursors is introduced into the processing chamber and the plasma is ignited to etch the substrate.

During the process steps, the temperature of various components of the system and the substrate itself may vary. These temperature variations may have undesired effects on the resulting substrate (eg, non-uniform CD). The temperature change can produce a desired effect on the substrate. For example, if non-uniformities occur in the substrate prior to etching, spatial control of the temperature and etch process can be used to correct for the non-uniformities. Accordingly, a substrate processing system may implement various systems and methods for estimating the temperature of various components and substrates during processing.

A temperature controller for a substrate support in a substrate processing system includes a memory storing a first model that correlates the temperature of a plurality of first thermal control elements (TCEs) disposed in the substrate support with the substrate support The first temperature response of the component is correlated. The first temperature response corresponds to a location on the surface of the substrate support. The temperature estimation module calculates a resistance of the first TCE, determines a temperature of the first TCE based on the calculated resistance, and estimates an actual temperature response of the substrate support using the stored first model and the determined temperature of the first TCE. The temperature controller is configured to control the first TCE based on the actual temperature response of the substrate support.

A method of estimating a temperature of a substrate support in a substrate processing system includes storing a first model that compares the temperature of a plurality of first thermal control elements (TCEs) disposed in the substrate support to a first temperature of the substrate support. correlated with the temperature response. The first temperature response corresponds to a location on the surface of the substrate support. The method further includes calculating a resistance of the first TCE, determining a temperature of the first TCE based on the calculated resistance, estimating an actual temperature response of the substrate support using the stored first model and the determined temperature of the first TCE, and The first TCE is controlled in response to the actual temperature of the support.

Further scope of applicability of the present disclosure will become apparent from the detailed description, claims, and drawings. The detailed implementations and specific examples are for the purpose of description only, and are not intended to limit the scope of the present disclosure.

100: system

102: processing chamber

104: Upper electrode

106: Electrostatic chuck (ESC)

108: Substrate

109: sprinkler head

110: Bottom plate

112: heating plate

114: thermal resistance layer

116: channel

120:RF generation system

122: RF voltage generator

124: Matching and distribution network

130: Gas delivery system

132-1: Gas source

132-2: Gas source

132-N: Gas source

132: Gas source

134-1: Valve

134-2: valve

134-N: valve

134: valve

136-1: Mass Flow Controller

136-2: Mass Flow Controller

136-N: Mass Flow Controller

136: Mass flow controller

140: Manifold

142: Temperature controller

144: Thermal Control Element (TCE)

146:Coolant assembly

150: valve

152: pump

160: system controller

170:Robot Arm

172: Load lock chamber

200:ESC

204: Temperature controller

208: connector

212-1: Juguan TCE

212-2: Giant TCE

212-3: Juguan TCE

212-4: Giant TCE

212: Giant TCE

216: Microscopic TCE

224: bottom plate

228:RF plasma source

232: Bias RF source

236-1: District

236-2: District

236-3: District

236-4: District

236: area

240: channel

244: thermal resistance layer

248: ceramic heating plate

252: first floor

256: second layer

300: temperature controller

304: Giant TCE controller

308: Microscopic TCE Controller

312: memory

316: interface

320:Temperature Estimation Module

324: input

328: sensor

400:Temperature estimation module

404:Module

408: input

412: base plate temperature

416: Bias RF power

420:TCP RF Power

424: voltage and current measurement value

428: Power input

432:TCP RF module

436: Bias RF module

440: base plate temperature module

444: Microscopic TCE temperature module

448:Zone temperature module

452: Resistance module

456: resistance to temperature module

460: sum node

464: Esc temperature estimate

700: Temperature response

704: temperature

900: method

904: block

908: block

912: cube

916: cube

920: block

924: block

928: cube

932: cube

936: block

940: block

944: cube

A more complete understanding of the present disclosure will be obtained from the detailed description and accompanying drawings, in which: FIG. 1 is a functional block diagram of an exemplary substrate processing system including an electrostatic chuck according to the principles of the present disclosure; FIG. 2A is an exemplary electrostatic chuck in accordance with principles of the present disclosure; FIG. 2B illustrates regions and macroscopic thermal control elements of an exemplary electrostatic chuck in accordance with principles of the present disclosure; FIG. 2C illustrates an illustration in accordance with principles of the present disclosure 3 is an exemplary temperature controller according to the principles of the present disclosure; FIG. 4 is an exemplary temperature estimation module according to the principles of the present disclosure; FIG. 5A illustrates Measured voltage and current for each of multiple temperatures of the thermal control element in accordance with the principles of the disclosure; FIG. Calculated resistance; FIG. 6 illustrates the relationship between resistance and temperature of a thermal control element in accordance with principles of the present disclosure; 7 illustrates an exemplary temperature response at a surface location of an electrostatic chuck in accordance with principles of the present disclosure; FIG. 8 illustrates the temperature of an electrostatic chuck estimated using an exemplary model in accordance with principles of the present disclosure; and FIG. 9 illustrates Steps of an Exemplary Temperature Estimation Method According to the Principles of the Disclosure.

In the drawings, reference numbers may be reused to identify similar and/or identical elements.

In a substrate processing system, the temperature of a substrate support, such as an electrostatic chuck (ESC), can be controlled during process steps. For example, different processes and individual steps may require substrates to be maintained at different temperatures. The contact surface temperature of the ESC can be controlled to maintain the substrate at a desired temperature. By way of example only, an ESC may include a heating plate (eg, a ceramic heating plate). The substrate may be placed on a hot plate. Accordingly, the temperature of the heating plate is controlled to achieve the desired temperature of the substrate.

Variations in the manufacturing process may cause corresponding changes in the characteristics of the heating plate and the effectiveness of the temperature control of the heating plate. For example, variations (i.e., non-uniformities) may include, but are not limited to, local variations in the thickness and/or thermal conductivity of layers in the structure of the heating plate, variations in the flatness of the machined surface, and/or variations within the heating plate. Variations in the characteristics of the individual thermal control elements (TCEs). These non-uniformities may lead to local differences in heat transfer (ie, local temperature non-uniformities), and thus to substrate temperature non-uniformities.

Other system changes may further affect temperature non-uniformity. Other system changes may include, but are not limited to: changes between different substrate processing chambers, changes between process steps (e.g. e.g. occurrence, type, amount, duration, etc. of plasma steps), difference between temperature inside chamber and ESC, variation of process parameters (e.g., power, frequency, etc.), differences between individual wafers, etc. , and/or changes in user input/restrictions.

It can be difficult to accurately control and/or measure some conditions within a substrate processing chamber (ie, in situ) during operation. Accordingly, the substrate processing system can perform virtual metrology to estimate conditions within the substrate processing chamber. For example, virtual metrology systems and methods can implement mathematical models that relate actual measured states (eg, in situ measurements using respective sensors) to other states and properties.

Systems and methods in accordance with principles of the present disclosure perform virtual metrology to estimate the temperature of the ESC (eg, the surface temperature of the ESC, which may correspond to the temperature of a wafer being processed on the ESC). For example, some substrate processing systems may implement a combination of macroscopic TCE and microscopic TCE to compensate for temperature non-uniformities in the ESC. In an exemplary embodiment, an ESC including one or multiple zones (eg, a multi-zone ESC) may include individual macroscopic TCEs for each zone of the heating plate, and a plurality of microscopic TCEs distributed throughout the heating plate. A plurality of microscopic TCEs (which may be referred to herein as "heaters") can be individually controlled to compensate for temperature non-uniformities in various regions of the ESC. The systems and methods of the present disclosure model ESC temperature as a function of the operating characteristics of the microscopic TCE. For example, microscopic TCEs in accordance with principles of the present disclosure may comprise materials with high thermal sensitivity (eg, tungsten metal alloys).

In this way, the temperature of the substrate during processing can be more accurately estimated since process variations affect the relationship between the substrate, the ESC, and other elements of the substrate processing system (e.g., substrate temperature, power supplied to the substrate, etc.) . In some examples, additional temperature sensors in various regions of the ESC may be eliminated. Although described in terms of ESC temperature estimation, the principles of the present disclosure as described herein can also be applied to estimate other substrate processing variables such as wafer level bias RF voltages, etch rates, and the like.

Referring now to FIG. 1 , an exemplary substrate processing system 100 for performing etching using RF plasma is shown. The substrate processing system 100 includes a processing chamber 102 that surrounds other components of the substrate processing chamber 102 and houses an RF plasma. The substrate processing chamber 102 includes a top electrode 104 and a substrate support such as an electrostatic chuck (ESC) 106 . During operation, substrate 108 is disposed on ESC 106 .

By way of example only, the upper electrode 104 may include a showerhead 109 that directs and distributes process gases. The showerhead 109 may include a stem including an end connected to the top surface of the processing chamber. At a location spaced from the top surface of the processing chamber, a base portion is generally cylindrical and extends radially outward from opposite ends of the stem portion. The face substrate surface or panel of the base portion of the showerhead includes a plurality of holes through which process gas or flushing gas flows. Alternatively, the upper electrode 104 may comprise a conductive plate, and the process gases may be directed in another manner.

The ESC 106 includes a conductive base plate 110 as a lower electrode. The bottom plate 110 may support a heating plate 112, which may correspond to a ceramic multi-zone heating plate. A thermal resistance layer 114 can be disposed between the heating plate 112 and the bottom plate 110 . The base plate 110 may include one or more coolant channels 116 for flowing coolant through the base plate 110 .

The RF generation system 120 generates and outputs an RF voltage to one of the upper electrode 104 and the lower electrode (eg, the bottom plate 110 of the ESC 106 ). The other of the upper electrode 104 and the bottom plate 110 may be DC grounded, AC grounded, or floating. For example only, the RF generation system 120 may include an RF voltage generator 122 that generates an RF voltage that is fed to the top electrode 104 or the bottom plate 110 through a matching and distribution network 124 . In other examples, plasma can be generated inductively or remotely.

The gas delivery system 130 includes one or more gas sources 132-1, 132-2, . . . , and 132-N (collectively referred to as gas sources 132), where N is an integer greater than zero. A gas source supplies one or more precursors and mixtures thereof. The gas source can also supply flushing gas. Vaporized precursors may also be used. gas comes Source 132 passes through valves 134-1, 134-2, . . . , and 134-N (collectively valves 134) and mass flow controllers 136-1, 136-2, . 136) to manifold 140. The output of manifold 140 is fed to processing chamber 102 . By way of example only, the output of manifold 140 is fed to showerhead 109 .

The temperature controller 142 may be connected to a plurality of thermal control elements (TCE) 144 disposed in the heating plate 112 . For example, TCEs 144 may include, but are not limited to: individual macroscopic TCEs corresponding to individual zones in a multi-zone heating plate; and/or arrays of microscopic TCEs disposed in multiple zones of a multi-zone heating plate, This is described in further detail in Figures 2A and 2B. A temperature controller 142 may be used to control a plurality of TCEs 144 to control the temperature of the ESC 106 and substrate 108 .

The temperature controller 142 can communicate with the coolant assembly 146 to control the flow of coolant in the channel 116 . For example, coolant assembly 146 may include a coolant pump and reservoir. The temperature controller 142 operates the coolant assembly 146 to selectively flow coolant through the passage 116 to cool the ESC 106 .

The reactants may be evacuated from the processing chamber 102 using a valve 150 and a pump 152 . The elements of the substrate processing system 100 may be controlled using a system controller 160 . Robotic arm 170 may be used to transport substrates onto and remove substrates from ESC 106 . For example, robotic arm 170 may transfer substrates between ESC 106 and load lock chamber 172 . Although shown as a separate controller, temperature controller 142 may be implemented within system controller 160 . Temperature controller 142 may be further configured to implement one or more models to estimate the temperature of ESC 106 in accordance with the principles of the present disclosure.

Referring now to Figures 2A, 2B, and 2C, an exemplary ESC 200 is shown. The temperature controller 204 communicates with the ESC 200 via one or more electrical or communication connections 208 . For example, electrical connections 208 may include connections for providing voltage/power to control Macro TCEs 212-1, 212-2, 212-3, and 212-4 (collectively Macro TCEs 212) And/or TCE 216 in Micro. Connector 208 may further include a connector for receiving feedback, such as: from backplane 224 (by way of example only, from a backplane sensor) Temperature feedback, measurements of voltage and/or current supplied to the microscopic TCE 216, feedback indicative of RF power supplied from an RF plasma source (e.g., a transformer coupled plasma source or TCP source) 228, indicative of RF power from a bias RF Source 232 provides feedback of bias RF power to ESC 200, and so on. Although microscopic TCEs 216 are shown in FIGS. 2B and 2C aligned with macroscopic TCEs 212 in a circular, concentric configuration, other configurations of microscopic TCEs 216 relative to macroscopic TCEs 212 may be used.

As shown, ESC 200 is a multi-zone ESC that includes zones 236-1, 236-2, 236-3, and 236-4 (collectively, zones 236). Although shown as four concentric zones 236 , in embodiments, ESC 200 may include one, two, three, or more than four zones 236 . The shape of region 236 may vary. For example, zones 236 may be arranged in four quadrants or another grid-like configuration. Each zone 236 includes (by way of example only) a respective one of the macroscopic TCEs 212 . For example, the base plate 224 includes coolant channels 240 , a thermal resistance layer 244 formed over the base plate 224 , and a multi-zone ceramic heating plate 248 formed over the thermal resistance layer 244 . The heating plate 248 may include a plurality of bonding layers including a first layer 252 as shown in FIG. 2B, and a second layer 256 as shown in FIG. 2C. The first layer 252 includes macroscopic TCEs 212 , while the second layer 256 includes a plurality of microscopic TCEs 216 .

Temperature controller 204 controls macroscopic TCE 212 and microscopic TCE 216 according to a desired set point temperature. For example, temperature controller 204 may receive (eg, from system controller 160 as shown in FIG. 1 ) a setpoint temperature for one or more zones 236 . For example only, temperature controller 204 may receive the same setpoint temperature for all or several zones 236 , and/or different individual setpoint temperatures for each zone 236 . The setpoint temperature for each zone 236 may vary across different processes, and across different steps of each process.

The temperature controller 204 controls the macro TCE 212 for each zone 236 based on the individual set point temperature and temperature feedback. For example, the temperature controller 204 independently adjusts the power (eg, current) provided to each of the macroscopic TCEs 212 to achieve the set point temperature. Juguan TCE 212 can each include a single Resistive coils or other structures are schematically represented by dotted lines in FIG. 2B. Accordingly, adjusting one of the macroscopic TCEs 212 affects the temperature of the entire individual zone 236 and possibly other ones of the zones 236 as well.

On the other hand, temperature controller 204 may individually control each of microscopic TCEs 216 to locally adjust the temperature of region 236 . For example, while individual microscopic TCEs 216 may be located entirely within one of the zones 236 , adjusting the thermal output of any one of the microscopic TCEs 216 can have a thermal effect on multiple zones 236 and locations across the heating plate 248 . Accordingly, one or more microscopic TCEs 216 may be selectively activated and/or deactivated to further adjust the temperature of zone 236 . As will be described in further detail below, temperature controller 204 implements systems and methods in accordance with the present disclosure to estimate the temperature of ESC 200 .

Referring now to FIG. 3, an exemplary temperature controller 300 in accordance with the principles of the present disclosure includes a macroscopic TCE controller 304 and a microscopic TCE controller 308 (which in an embodiment may be implemented as a single controller), memory 312, and interface 316 (for communicating with system controller 160 such as shown in FIG. 1 , for receiving user input, and the like), and ESC temperature estimation module 320 . By way of example only, memory 312 may include non-volatile memory such as flash memory. Temperature controller 300 receives process setpoint temperatures (eg, desired setpoint temperatures for individual process steps) and/or other parameters from system controller 160 via interface 316 . Interface 316 provides process setpoint temperature to macro TCE controller 304 . The process setpoint temperatures may include a single setpoint temperature for each zone 236 and/or different process setpoint temperatures for each of the individual zones 236 . The macro TCE controller 304 controls the macro TCE 212 according to the received process setpoint or process setpoints. The microscopic TCEs 216 may then be controlled to achieve process setpoints in each zone 236 to compensate for temperature non-uniformities in the zones 236 .

ESC temperature estimation module 320 estimates the temperature of ESC 200 based on feedback provided by macro TCE controller 304 , micro TCE controller 308 , and one or more inputs 324 . The estimated ESC temperature may depend on, for example, the zone temperature (i.e., the temperature in zone 236 as controlled by macro TCE controller 304), Local temperature (ie, as controlled by microscopic TCE controller 308), bias RF power, TCP RF power, and chassis temperature. The temperature estimation module 320 estimates the ESC temperature based on individual models (eg, stored in memory 312 ) for each of the inputs related to the ESC temperature. By way of example only, each of the models associates individual temperature contributions to the ESC temperature for each of the inputs.

In one example, the temperature estimation module 320 receives readings of voltage and current associated with each of the microscopic TCEs 216 . For example, temperature estimation module 320 may receive readings of the voltage supplied from micro-TCE controller 308 to micro-TCE 216 and may receive a reading of the voltage flowing through the micro-TCE 216 via a respective current sensor 328 (which is connected in series with micro-TCE 216 ). A measurement of the current of the TCE 216 . The individual resistance of each of the microscopic TCEs 216 can then be calculated (eg, using a model) from the received voltage and current information. Since the microscopic TCEs 216 include resistive heating elements, the resistance of each of the microscopic TCEs 216 is representative of the temperature of the ESC 200 in the corresponding location. In other words, the resistance of each of the microscopic TCEs 216 is a function of temperature, and thus, the calculated resistance of each of the microscopic TCEs 216 can be mapped to the corresponding temperature. As will be described in further detail below, the temperature estimation module 320 calculates the temperature contribution to the ESC temperature for each of the microscopic TCE temperatures.

Referring now to FIG. 4, an exemplary ESC temperature estimation module 400 includes one or more modules 404 configured to receive a respective one of inputs 408 and generate a corresponding contribution to the ESC temperature therefrom, which may be referred to as in response to temperature. The temperature responses may each correspond to a product of model coefficients (eg, G1 , G2 , G3 , G4 , and G5 ) and a respective one of the inputs 408 . For example, inputs 408 may include base plate temperature 412 , bias RF power 416 , TCP RF power 420 , voltage and current measurements 424 for each of microscopic TCEs 216 , and power input 428 to macroscopic TCE 212 .

Modules 404 may include TCP RF module 432 , bias RF module 436 , base plate temperature module 440 , micro TCE temperature module 444 and zone temperature module 448 . The temperature estimation module 400 may further include electrical Resistance module 452 and resistance-to-temperature module 456 . For example, the resistance module 452 calculates the individual resistance of each of the microscopic TCEs 216 based on the corresponding voltage and current measurements 424 . The resistance-to-temperature module 456 calculates the temperature based on the resistance calculated by the resistance module 452 (eg, using a map relating resistance to temperature for each of the microscopic TCEs 216 ). Resistance versus temperature module 456 provides the calculated resistance to microscopic TCE temperature module 444 .

Each of modules 404 implements a separate model to generate and output a temperature response based on respective inputs 408 . As an example only, as will be described in further detail below, the models implemented by modules 444, 448, 440, 436, and 432 are represented by Gl, G2, G3, G4, and G5, respectively. The outputs of the modules 404 are summed together at a summing node 460 to generate an ESC temperature estimate 464 . The temperature of the ESC 200 may be further controlled using the ESC temperature estimate 464 to achieve a desired temperature. For example, the voltage/power provided to the macro-TCE 212 and the micro-TCE 216 may be adjusted based on the ESC temperature estimate 464 to more accurately achieve the desired temperature.

Accordingly, the temperature estimate 464 ("temperature") corresponds to: temperature = G1 * micro temperature + G2 * macro power + G3 * base plate temperature + G4 * bias power + G5 * TCP power, where micro temperature, macro Power, chassis temperature, bias power, and TCP power correspond to inputs 408 to modules 444, 448, 440, 436, and 432, respectively. Accordingly, each of the inputs 408 is modified by a respective one of the models Gl, G2, G3, G4, and G5.

，其中k為與巨觀TCE 212相關的受控體增益(plant gain)，L為與巨觀TCE 212相關的時間延遲，而T為與巨觀TCE 212相關的時間常數。例如，時間延遲可對應至溫度響應延遲。模型G3可對應於

，其中k_base為與底板相關的受控體增益，L_base為與底板相關的時間延遲，而T_base為與底板相關的時間常數。模型G4可對應 於

，其中k_bias為與偏壓RF功率相關的受控體增益，L_bias為與偏壓RF功率相關的時間延遲，而T_bias為與偏壓RF功率相關的時間常數。模型G5可對應於

，其中k_tcp為與TCP RF功率相關的受控體增益，L_tcp為與TCP RF功率相關的時間延遲，而T_tcp為與TCP RF功率相關的時間常數。 As an example only, model G2 may correspond to

, where k is the plant gain associated with the macroscopic TCE 212 , L is the time delay associated with the macroscopic TCE 212 , and T is the time constant associated with the macroscopic TCE 212 . For example, the time delay may correspond to a temperature response delay. Model G3 may correspond to

, where k _base is the controlled object gain related to the base plate, L _base is the time delay related to the base plate, and T _base is the time constant related to the base plate. Model G4 may correspond to

, where k _bias is the plant gain related to the bias RF power, L _bias is the time delay related to the bias RF power, and T _bias is the time constant related to the bias RF power. Model G5 may correspond to

, where k _tcp is the plant gain related to the TCP RF power, L _tcp is the time delay related to the TCP RF power, and T _tcp is the time constant related to the TCP RF power.

As shown in FIG. 5A , model G1 can be calculated by measuring the voltage and current for each of the microscopic TCEs 216 at complex temperatures. In one example, an array of microscopic TCEs 216 (e.g., corresponding to microscopic TCEs 216 embedded within ESC 200, as shown in FIGS. 2A, 2B, and 2C) may be provided in an oven or process chamber configured to maintain a desired temperature. within. With the oven at each of a plurality of temperatures (eg, in the range from -40 to 130° C.), a voltage is applied to the microscopic TCE 216 and the corresponding current is measured. In this way, the respective voltage and current for each of the microscopic TCEs 216 at each of the individual temperatures may be determined.

The measured voltage and current can then be used to calculate the resistance at each temperature. 5B illustrates the relationship between resistance and voltage calculated for each of the complex temperatures. From this, temperature sensitivity (ie, the sensitivity of the resistance of selected ones of the microscopic TCEs 216 to temperature changes) can be determined. This determination may be performed for one of the micro-TCEs 216, for a predetermined portion of the micro-TCEs 216, for all micro-TCEs 216, and so forth. As shown in FIG. 6, the relationship between the resistance of the microscopic TCE 216 and the corresponding temperature is approximately linear. In this manner, resistance versus temperature module 456 may be implemented as a map or model that correlates resistance (e.g., calculated based on voltage and current measurements 424) to estimated temperature.

The temperature response at individual locations of the ESC 200 may be determined for each of the microscopic TCEs 216 . For example, a thermocouple or other temperature sensor may be disposed on the upper surface of the ESC 200 at a location corresponding to a selected one of the microscopic TCEs 216 . FIG. 7 illustrates temperature 704 relative to counterparts in microscopic TCEs 216 The temperature response 700 at the surface location of the ESC 200. As shown, the temperature 704 is offset (ie, greater than) the temperature response 700 of the ESC 200 by approximately 5-8°C. Further, the temperature response of the microscopic TCE 216 is faster than the temperature response 700 of the ESC 200 . For example, temperature response 700 may have a delay of approximately 4 seconds relative to an increase in temperature 704 . As shown, the time constant of the temperature response of the microscopic TCE is about 9 seconds, while the time constant of the temperature response 700 is about 14 seconds.

其中偏移、增益、延遲L對應於預定的常數，而s是單位為秒的時間。例如，偏移量可對應至溫度704與溫度響應700之間的偏移。圖8使用模型G1說明相對於ESC 200的實際測量溫度之ESC 200的估計溫度。儘管此處描述的是線性動態模型，但在一些範例中，一或更多模型G1、G2、G3、G4及G5可對應至其他模型類型，例如高階模型(higher order models)、非線性模型等等。 Model G1 is calculated from the observed relationship (eg, as shown in FIG. 7 ) between the temperature response 700 of the ESC 200 and the estimated temperature 704 of the corresponding microscopic TCE 216 . In one example, model G1 may correspond to: G 1 = offset + gain *

where the offset, gain, and delay L correspond to predetermined constants, and s is the time in seconds. For example, an offset may correspond to an offset between temperature 704 and temperature response 700 . FIG. 8 illustrates the estimated temperature of the ESC 200 relative to the actual measured temperature of the ESC 200 using model G1. Although linear dynamic models are described here, in some examples, one or more models G1, G2, G3, G4, and G5 can be mapped to other model types, such as higher order models, nonlinear models, etc. Wait.

Referring now to FIG. 9 , the example ESC temperature estimation method 900 begins at block 904 . At block 908 , the method 900 determines the voltage and current of the plurality of heating elements or the plurality of heaters (eg, microscopic TCE 216 ) of the ESC at the plurality of temperatures. At block 912 , method 900 uses the determined voltage and current to calculate the resistance of microscopic TCE 216 at each of the complex temperatures. At block 916, the method stores data representing the relationship between the voltage and the calculated resistance at each of the complex temperatures. For example, the stored data may be incorporated into a map or model implemented by resistance versus temperature module 456 . At block 920 , the method 900 determines and stores a model that relates the temperature of the microscopic TCE 216 to individual surface locations on the ESC 200 .

At block 924, the method 900 (eg, the ESC temperature estimation module 400) determines the voltage and current of the microscopic TCE 216 during substrate processing. At block 928, the method 900 (eg, the resistance module 452) determines the resistance of the microscopic TCE 216 based on the determined voltage and current. At block 932 , the method 900 (eg, the resistance versus temperature module 456 ) determines the temperature of the microscopic TCE 216 based on the resistance. At block 936 , the method 900 (eg, the micro-TCE temperature module 444 implementing the stored model) determines an individual surface temperature response of the ESC 200 based on the determined temperature of the micro-TCE 216 . At block 940 , method 900 (eg, temperature estimation module 400 ) generates and outputs an estimated temperature of ESC 200 . For example, an estimated temperature of the ESC 200 may be generated based on the temperature response determined at block 936, as well as temperature responses calculated for other inputs using individual models G2, G3, G4, and G5 (as described above in FIG. 4). Method 900 ends at block 944 .

The foregoing description is merely illustrative in nature and is not intended to limit the invention, its application or uses. The broad teachings of the invention can be implemented in a wide variety of forms. Therefore, even though this disclosure contains particular examples, the true scope of the invention should not be so limited since other modifications will become apparent upon a study of the drawings, specification and following claims. It should be understood that one or more steps in a method can be performed in a different order (or simultaneously) without changing the principles of the present invention. Furthermore, while each embodiment has been described above as having certain features, any or more of those features described with respect to any embodiment of the invention may be practiced in any other embodiment, And/or can be combined with features of any other embodiment, even if the combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and substitutions of one or more embodiments for each other are still within the scope of the present invention.

Spatial and functional relationships between elements (eg, between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including "connected," "engaged," "coupled," "adjacent" ”, “Close”, “On Top”, “Above”, “Below”, and “Settings”. Unless expressly described as "directly", when the above disclosure describes the relationship between the first and second elements When there is a relationship, the relationship may be a direct relationship with no other intermediate elements between the first and second elements, or there may be one or more intermediate elements (spatial or functional) between the first and second elements. above) indirect relationship. As used herein, the words "at least one of A, B, and C" should be interpreted as logic (A or B or C) using the non-exclusive logic symbols OR, and should not be construed to mean "at least one of A, At least one of B, and at least one of C."

In some embodiments, the controller is part of a system, which may be part of one of the aforementioned examples. Such systems may include semiconductor processing equipment including: a processing tool (or processing tools), a chamber (or chambers), a bench (or tool tables) for processing, and/or specific processing elements ( such as wafer holders, airflow systems, etc.). These systems can be combined with electronics to control the operation of the system before, during, and after processing of semiconductor wafers or substrates. These electronic devices may be referred to as "controllers," which may control various elements or subcomponents of the system (or systems). Depending on the process requirements and/or type of system, the controller can be programmed to control any of the processes disclosed herein, including process gas delivery, temperature settings (e.g., heating and/or cooling), pressure settings, vacuum settings, power settings, radio frequency (RF) generator settings, RF matching circuit settings, frequency settings, flow rate settings, fluid delivery settings, positioning and operation settings, access tools and other delivery tools connected to or interfaced with a particular system and/or Or wafer transfer in load lock chamber.

Broadly speaking, a controller can be defined as an electronic device having various integrated circuits, logic, memory, and/or software for receiving commands, sending commands, controlling operations, enabling cleaning operations, allowing endpoint measurements, and the like. The integrated circuits may include one of chips in the form of firmware storing program instructions, digital signal processors (DSPs), chips defined as application specific integrated circuits (ASICs), and/or executing program instructions (such as software) or More microprocessors or microcontrollers. Program instructions can be instructions to communicate with the controller in the form of various individual settings (or program files), which are defined to be used on the semiconductor wafer, or on the target Operating parameters for a specific process on a semiconductor wafer, or on a system. In some embodiments, the operating parameters may be part of a recipe defined by a process engineer for use in one or more layers, materials, metals, oxides, silicon, silicon dioxide, surfaces, circuits , and/or dies of a wafer, one or more processing steps are performed.

In some embodiments, the controller can be part of or connected to a computer that is integrated with the system, connected to the system, or connected to the system through a network, or a combination thereof. For example, the controller can be located "in the "cloud", or be all or part of the fab's mainframe computer system, which allows remote access for wafer processing. The computer can achieve remote access to the system to monitor the current process of manufacturing operations, view the history of past manufacturing operations, view trends or performance indicators from multiple manufacturing operations, to change the parameters of the current process, to set the process Step to continue the current process, or start a new process. In some examples, a remote computer (eg, a server) can provide the process recipe to the system through a network, which can include a local area network or the Internet. The remote computer may include a user interface that enables input or programming of parameters and/or settings that are then transmitted from the remote computer to the system. In some examples, the controller receives instructions in the form of data specifying parameters for each of the processing steps to be performed during one or more operations. It should be appreciated that these parameters may be specific to the type of process to be performed, and the type of tool with which the controller is configured to interface with or control the tool. Thus, as noted above, the controller can be decentralized, eg, by including one or more separate controllers that are networked together and work toward a common goal, such as the process and control described herein. An example of a separate controller for such purposes could be one or more integrated circuits on the chamber, which are connected to one or more remote (eg, at platform level, or part of a remote computer) location. Multiple integrated circuits are connected, which combine to control the processes on the chamber.

Exemplary systems may include a plasma etch chamber or module, a deposition chamber or module, a spin rinse chamber or module, a metal plating chamber or module, a clean chamber or module, a bevel etch chamber or Module, Physical Vapor Deposition (PVD) Chamber or Module, Chemical Vapor Deposition (CVD) Chamber or Module, Atomic Layer Deposition (ALD) Chamber or Module, Atomic Layer Etching (ALE) Chamber or Modules, ion implantation chambers or modules, track chambers or modules, and any other semiconductor processing system that may be used in connection with or in the fabrication and/or production of semiconductor wafers, without limitation.

As noted above, depending on the process step (or process steps) to be performed by the tool, the controller may communicate with one or more of: other tool circuits or modules, other tool components, cluster tools, other tool interfaces, pulling tools , an adjacent tool, a tool throughout the fab, a main computer, another controller, or a tool for material transfer that brings containers of wafers to or from tool locations and/or load ports in a semiconductor fab.

200‧‧‧Electrostatic chuck (ESC)

204‧‧‧temperature controller

208‧‧‧connector

212‧‧‧Juguan TCE

216‧‧‧Microscopic TCE

224‧‧‧Bottom

228‧‧‧RF plasma source

232‧‧‧bias RF source

240‧‧‧channels

244‧‧‧thermal resistance layer

248‧‧‧Ceramic heating plate

Claims (16)

A temperature controller for a substrate support in a substrate processing system, the temperature controller comprising: a memory for storing a first model of (i) disposed in the substrate support temperatures of a plurality of first thermal control elements (TCEs) are correlated with (ii) a plurality of first temperature responses of the substrate support, wherein the first temperature responses correspond to a plurality of locations on a surface of the substrate support, and wherein the first TCEs are configured to heat the substrate support; and a temperature estimation module that (i) calculates the resistance of the first TCEs, (ii) determines the the temperature of the first TCE, and (iii) using the stored first model and the temperatures of the first TCEs determined based on the calculated resistances to estimate an actual temperature response of the substrate support, wherein The temperature controller is configured to control the first TCEs to heat the substrate support based on the actual temperature response of the substrate support. The temperature controller according to claim 1, wherein: the memory further stores at least one of the following: a second model, which provides (i) to a plurality of second TCEs disposed in the substrate support and (ii) the plurality of second temperature responses of the substrate support, a third model that combines (i) the temperature of a bottom plate of the substrate support with (ii) the plurality of second temperature responses of the substrate support Three temperature responses are correlated, a fourth model that relates (i) a bias radio frequency (RF) power supplied to the substrate support to (ii) a plurality of fourth temperature responses of the substrate support, and A fifth model that relates (i) plasma RF power supplied to the substrate processing system to (ii) a plurality of fifth temperature responses of the substrate support. For the temperature controller of claim 2, wherein, in order to estimate the actual temperature response of the substrate support, the temperature estimation module is further based on the second model, the third model, the fourth model and the first model At least one of five models is stored to estimate the actual temperature response. The temperature controller of claim 2, wherein the temperature estimation module is based on the output value of at least one of the second model, the third model, the fourth model and the fifth model and the first The output values of the model are summed to estimate the actual temperature response. Such as the temperature controller of claim 2, wherein at least one of the second model, the third model, the fourth model and the fifth model corresponds to

, where k is the plant gain, L is the time delay, and T is the time constant. Such as the temperature controller of item 5 of the scope of the patent application, wherein the output value of at least one of the second model, the third model, the fourth model and the fifth model corresponds to

Multiplied with the respective input values. Such as the temperature controller of item 1 of the scope of the patent application, wherein the first model corresponds to the offset + gain *

, where the offset corresponds to an offset between the temperatures of the first TCEs and the first temperature responses, the gain amount corresponds to a gain of the first model, L corresponds to a time delay, and s corresponds to The time in seconds. A temperature controller as claimed in claim 1, wherein the memory stores a second model that relates (i) the calculated resistances to (ii) the temperatures of the first TCEs connected, and wherein the temperature estimation module uses the second model and the calculated resistances to determine the temperatures of the first TCEs. A method of estimating temperature for estimating the temperature of a substrate support in a substrate processing system, the method comprising: storing a first model of (i) a plurality of numbers disposed in the substrate support The temperature of a first thermal control element (TCE) is correlated with (ii) a plurality of first temperature responses of the substrate support, wherein the first temperature responses correspond to a plurality of locations on a surface of the substrate support, wherein The first TCEs are configured to heat the substrate support; calculating the resistance of the first TCEs; determining the temperature of the first TCEs based on the calculated resistance; using the stored first model, and based on estimating an actual temperature response of the substrate support for the temperatures of the first TCEs determined by the calculated resistances; and controlling the first TCEs to heat the substrate support based on the actual temperature response of the substrate support Substrate support. The method for estimating temperature according to claim 9, further comprising: storing at least one of the following: a second model, which will (i) provide power to a plurality of second TCEs disposed in the substrate support In association with (ii) the plurality of second temperature responses of the substrate support, a third model that combines (i) the temperature of a base plate of the substrate support with (ii) the plurality of third temperatures of the substrate support Responses are correlated, a fourth model that relates (i) a bias radio frequency (RF) power supplied to the substrate support to (ii) a plurality of fourth temperature responses of the substrate support, and a A fifth model that relates (i) plasma RF power supplied to the substrate processing system to (ii) a plurality of fifth temperature responses of the substrate support. The method for estimating temperature as claimed in claim 10, wherein the step of estimating the actual temperature response of the substrate support includes: further based on the second model, the third model, the fourth model and the fifth model wherein The at least one stored is used to estimate the actual temperature response. The method for estimating temperature as claimed in claim 10, wherein the step of estimating the actual temperature response includes: based on the output of at least one of the second model, the third model, the fourth model and the fifth model value and the output value of the first model to estimate the actual temperature response. The method for estimating temperature according to claim 10, wherein at least one of the second model, the third model, the fourth model and the fifth model corresponds to

, where k is the controlled body gain, L is the time delay, and T is the time constant. The method for estimating temperature as claimed in claim 13, wherein the output value of at least one of the second model, the third model, the fourth model and the fifth model corresponds to

Multiplied with the respective input values. A method for estimating temperature as claimed in claim 9, wherein the first model corresponds to offset + gain *

, where the offset corresponds to an offset between the temperatures of the first TCEs and the first temperature responses, the gain corresponds to a gain of the first model, L corresponds to a time delay, and s corresponds to The time in seconds. The method of estimating temperature as claimed in claim 9, further comprising storing a second model that relates (i) the calculated resistances to (ii) the temperatures of the first TCEs and further comprising determining the temperatures of the first TCEs using the second model and the calculated resistances.

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